Integrating scRNA-seq Datasets with Python

Author

Affiliation

Jack Leary

University of Florida

Published

January 21, 2024

Introduction

The need to integrate data from scRNA-seq samples into one harmonized dataset has increased in recent years as single cell sequencing has gotten cheaper, making it easier to collect data from multiple subjects, timepoints, or conditions. The goal of most integration techniques is to create an embedding that is (relatively) free of batch effects and that does not lead to the cells clustering by subject ID. There are many methods available to perform integration in both R & Python, and evaluating which method is “best” for your dataset can be tricky & subjective. In this tutorial we’ll try out several of the integration methods available in the scanpy package and compare their results. We’ll use a SmartSeq2 dataset containing myeloid cells from the developing human fetal liver that was originally analyzed in Popescu et al (2020). As such, our goal will be to determine which integration method results in the best embedding for downstream trajectory analysis. In developmental biology settings, a “good” scRNA-seq integrated embedding is smoothly connected (i.e., no spaced-out, discrete clusters), and places celltypes in roughly the order expected based on known biology.

Libraries

R

Code

library(dplyr)       # dataframe tools
library(Matrix)      # sparse matrices
library(Seurat)      # scRNA-seq tools
library(ggplot2)     # plots
library(reticulate)  # Python interface

Python

Code

import scvi                          # VAE integration

/Users/jack/Desktop/PhD/Research/Python_Envs/personal_site/lib/python3.11/site-packages/scvi/_settings.py:63: UserWarning: Since v1.0.0, scvi-tools no longer uses a random seed by default. Run `scvi.settings.seed = 0` to reproduce results from previous versions.
  self.seed = seed
/Users/jack/Desktop/PhD/Research/Python_Envs/personal_site/lib/python3.11/site-packages/scvi/_settings.py:70: UserWarning: Setting `dl_pin_memory_gpu_training` is deprecated in v1.0 and will be removed in v1.1. Please pass in `pin_memory` to the data loaders instead.
  self.dl_pin_memory_gpu_training = (

Code

import warnings                      # filter out warnings
import numpy as np                   # matrix utilities
import scanpy as sc                  # scRNA-seq processing
import pandas as pd                  # dataframe tools
import anndata as ad                 # scRNA-seq data structures
import matplotlib.pyplot as plt      # plot utilities
from scipy.sparse import csr_matrix  # sparse matrices

Code

warnings.simplefilter('ignore', category=UserWarning)

Theme for `matplotlib`

Here we define a theme for matplotlib that mostly matches ggplot2::theme_classic().

Code

base_size = 12
plt.rcParams.update({
    # font
    'font.size': base_size, 
    'font.weight': 'normal',
    # figure
    'figure.dpi': 300, 
    'figure.edgecolor': 'white', 
    'figure.facecolor': 'white', 
    'figure.figsize': (6, 4), 
    'figure.constrained_layout.use': True,
    # axes
    'axes.edgecolor': 'black',
    'axes.grid': False,
    'axes.labelpad': 2.75,
    'axes.labelsize': base_size * 0.8,
    'axes.linewidth': 1.5,
    'axes.spines.right': False,
    'axes.spines.top': False,
    'axes.titlelocation': 'left',
    'axes.titlepad': 11,
    'axes.titlesize': base_size,
    'axes.titleweight': 'normal',
    'axes.xmargin': 0.1, 
    'axes.ymargin': 0.1, 
    # legend
    'legend.borderaxespad': 1,
    'legend.borderpad': 0.5,
    'legend.columnspacing': 2,
    'legend.fontsize': base_size * 0.8,
    'legend.frameon': False,
    'legend.handleheight': 1,
    'legend.handlelength': 1.2,
    'legend.labelspacing': 1,
    'legend.title_fontsize': base_size, 
    'legend.markerscale': 1.25
})

Data

We’ll start by reading in the SmartSeq2 scRNA-seq data from Popescu et al, which we downloaded from the Human Developmental Cell Atlas portal. Some massaging of the data is necessary to get it ready to pass into Python.

Code

seu_blood <- readRDS("../../datasets/fetal_liver_SS2.RDS")
class(seu_blood) <- "Seurat"
blood_counts <- as.matrix(seu_blood@raw.data)
blood_counts <- blood_counts[, colnames(seu_blood@scale.data)]
cell_metadata <- seu_blood@meta.data[colnames(seu_blood@scale.data), ] %>% 
                 mutate(cell = rownames(.), 
                        .before = 1)
gene_metadata <- data.frame(gene = rownames(blood_counts))

Read data into Python

Using the reticulate R package we transfer our raw counts matrix and cell & gene metadata into Python, then create an AnnData object. Lastly, we rename some cell metadata features and subset to just the macrophage development lineage. This will simplify our trajectory structure and make choosing a good integration / embedding combination easier.

Code

blood_counts = csr_matrix(r.blood_counts.transpose())
cell_metadata = r.cell_metadata
gene_metadata = r.gene_metadata
ad_blood = ad.AnnData(blood_counts)
ad_blood.obs_names = cell_metadata['cell']
ad_blood.var_names = gene_metadata['gene']
ad_blood.obs = cell_metadata
ad_blood.obs.rename(columns={'cell.labels': 'celltype', 'fetal.ids': 'fetal_ID', 'percent.mito': 'percent_MT', 'sort.ids': 'sort_ID'}, inplace=True)
ad_blood = ad_blood[ad_blood.obs['celltype'].isin(['HSC_MPP', 'Neutrophil-myeloid progenitor', 'Monocyte precursor', 'Monocyte', 'Mono-Mac', 'Kupffer Cell'])]
ad_blood.obs['celltype'] = (
    ad_blood.obs['celltype']
              .map(lambda x: {'HSC_MPP': 'HSC', 'Mono-Mac': 'Monocyte-macrophage', 'Kupffer Cell': 'Kupffer cell'}.get(x, x))
              .astype('category')
)
ad_blood.var = gene_metadata
ad_blood

AnnData object with n_obs × n_vars = 486 × 33660
    obs: 'cell', 'nGene', 'nUMI', 'orig.ident', 'percent_MT', 'fetal_ID', 'sort_ID', 'tissue.id', 'plate.id', 'lanes', 'stages', 'sample.type', 'gender', 'cell.label', 'doublets', 'celltype'
    var: 'gene'

Preprocessing

After removing cells classified as doublets by the original authors, we filter out low-depth cells and genes expressed in less than 10 cells. We then set up a new layer named counts containing the raw counts - this is necessary for integration with scVI, which takes as input the raw, unnormalized expression estimates. Lastly, we identify 3,000 HVGs and subset our object to just those genes.

Code

ad_blood = ad_blood[ad_blood.obs['doublets'] == 'Singlet']
sc.pp.filter_cells(ad_blood, min_counts=200)
sc.pp.filter_genes(ad_blood, min_cells=10)
ad_blood.layers['counts'] = ad_blood.X.copy() 
ad_blood.raw = ad_blood
sc.pp.highly_variable_genes(
    ad_blood, 
    n_top_genes=3000, 
    flavor='seurat_v3', 
    layer='counts',
    subset=True
)

Integration with `scVI`

Paper, Docs

Train `scVI` model

We’ll integrate across the ID specifying which fetus the cells came from. We also make scVI aware of our celltypes, and instruct it to regress out variation associated with the percentage of mitochondrial reads.

Code

scvi.settings.verbosity = 0
scvi.settings.seed = 312

Code

scvi.settings.num_threads = 4
scvi.model.SCVI.setup_anndata(
    ad_blood, 
    layer='counts', 
    batch_key='fetal_ID', 
    labels_key='celltype', 
    continuous_covariate_keys=['percent_MT']
)

We allow gene dispersion estimates to vary by celltype, and specify expression as following a negative-binomial distribution.

Code

int_model = scvi.model.SCVI(
    ad_blood, 
    n_layers=3, 
    n_hidden=96, 
    n_latent=20, 
    gene_likelihood='nb', 
    dispersion='gene-label'
)

Finally we train a variational autoencoder (VAE) over 250 epochs to embed the cells in 20-dimensional latent space.

Code

int_model.train(
    early_stopping=True,
    accelerator='cpu', 
    max_epochs=250, 
    train_size=0.8
)

Code

ad_blood.obsm['X_scVI'] = int_model.get_latent_representation()

Code

sc.pl.embedding(
    ad_blood, 
    basis='scVI', 
    color='celltype',
    title='', 
    frameon=True, 
    size=30, 
    alpha=0.75, 
    show=False
)
plt.gca().set_xlabel('scVI 1')
plt.gca().set_ylabel('scVI 2')
plt.show()

SNN graph estimation

We use the cosine distance to identify 20 NNs for each cell in the latent scVI space. This neighbor graph will serve as the basis for our nonlinear embeddings.

Code

sc.pp.neighbors(
    ad_blood, 
    n_neighbors=20,
    n_pcs=None,  
    metric='cosine', 
    random_state=312, 
    use_rep='X_scVI'
)

UMAP embedding

Using default parameters, we fit a 2D UMAP embedding.

Code

sc.tl.umap(ad_blood, random_state=312)

The UMAP embedding is a bit oddly-shaped, but celltypes are well-connected and their progression follows known biology i.e., HSCs form precursor populations which develop into mature monocytic cells.

Code

sc.pl.embedding(
    ad_blood, 
    basis='umap', 
    color='celltype',
    title='', 
    frameon=True, 
    size=30, 
    alpha=0.75, 
    show=False
)
plt.gca().set_xlabel('UMAP 1')
plt.gca().set_ylabel('UMAP 2')
plt.show()

Force-directed graph embedding

With the Fruchterman-Reingold algorithm we generate a force-directed graph embedding in 2 dimensions. In my experience this algorithm often works better than UMAP at preserving trajectory structures.

Code

sc.tl.draw_graph(
    ad_blood, 
    layout='fr',  
    random_state=312,
    n_jobs=2
)

Similar to the UMAP embedding, the FR embedding is well-connected and the celltypes are arranged correctly. I like this embedding better, but that’s mostly based on aesthetics as well as some intuition about how pseudotime estimation would perform on each.

Code

sc.pl.draw_graph(
    ad_blood, 
    color='celltype', 
    title='', 
    alpha=0.75, 
    size=30, 
    show=False
)
plt.gca().set_xlabel('FR 1')
plt.gca().set_ylabel('FR 2')
plt.show()

Figure 3: scVI-based force-directed graph embedding

Diffusion map embedding

Lastly, we estimate a diffusion map embedding in 15 dimensions. This algorithm is specifically designed to preserve transitional structures, but in my experience it usually only works well on datasets with very simple trajectories.

Code

sc.tl.diffmap(
    ad_blood, 
    random_state=312, 
    n_comps=16
)
ad_blood.obsm['X_diffmap_old'] = ad_blood.obsm['X_diffmap']
ad_blood.obsm['X_diffmap'] = ad_blood.obsm['X_diffmap'][:, 1:]

Like the UMAP embedding the diffusion map embedding is pretty angular, but it recapitulates the biology well. Altogether, the scVI integration seems to have worked correctly as all embedding algorithms perform reasonably well.

Code

sc.pl.diffmap(
    ad_blood, 
    color='celltype', 
    title='', 
    alpha=0.75, 
    size=30, 
    show=False
)
plt.gca().set_xlabel('DC 1')
plt.gca().set_ylabel('DC 2')
plt.show()

Figure 4: scVI-based diffusion map embedding

Integration with `Harmony`

Paper, Docs

Normalization

The Harmony algorithm works by “correcting” a principal component embedding for batch effects. As such, we need to first normalize and variance-stabilize the data.

Code

sc.pp.normalize_total(ad_blood, target_sum=1e4)
sc.pp.log1p(ad_blood)

PCA embedding

Next we scale the normalized counts and run PCA.

Code

sc.pp.scale(ad_blood)
sc.tl.pca(
    ad_blood, 
    n_comps=30, 
    random_state=312, 
    use_highly_variable=True
)

The PCA embedding on its own is alright, and I don’t imagine the integration procedure will change it much.

Code

sc.pl.embedding(
    ad_blood, 
    basis='pca', 
    color='celltype', 
    title='', 
    frameon=True, 
    size=30, 
    alpha=0.75, 
    show=False
)
plt.gca().set_xlabel('PC 1')
plt.gca().set_ylabel('PC 2')
plt.show()

PCA correction with `Harmony`

Code

sc.external.pp.harmony_integrate(ad_blood, key='fetal_ID')

2023-12-08 17:54:12,890 - harmonypy - INFO - Computing initial centroids with sklearn.KMeans...
/Users/jack/Desktop/PhD/Research/Python_Envs/personal_site/lib/python3.11/site-packages/threadpoolctl.py:1019: RuntimeWarning: libc not found. The ctypes module in Python 3.11 is maybe too old for this OS.
  warnings.warn(
2023-12-08 17:54:12,945 - harmonypy - INFO - sklearn.KMeans initialization complete.
2023-12-08 17:54:12,946 - harmonypy - INFO - Iteration 1 of 10
2023-12-08 17:54:12,992 - harmonypy - INFO - Iteration 2 of 10
2023-12-08 17:54:13,037 - harmonypy - INFO - Iteration 3 of 10
2023-12-08 17:54:13,081 - harmonypy - INFO - Iteration 4 of 10
2023-12-08 17:54:13,127 - harmonypy - INFO - Iteration 5 of 10
2023-12-08 17:54:13,171 - harmonypy - INFO - Converged after 5 iterations

As expected, the corrected PCA space is pretty similar to the original one.

Code

sc.pl.embedding(
    ad_blood, 
    basis='pca_harmony', 
    color='celltype', 
    title='', 
    frameon=True, 
    size=30, 
    alpha=0.75, 
    show=False
)
plt.gca().set_xlabel('Harmony 1')
plt.gca().set_ylabel('Harmony 2')
plt.show()

SNN graph estimation

We compute \(k = 20\) NNs in the Harmony PCA space.

Code

sc.pp.neighbors(
    ad_blood, 
    n_neighbors=20,
    n_pcs=None,  
    metric='cosine', 
    random_state=312, 
    use_rep='X_pca_harmony'
)

UMAP embedding

Code

sc.tl.umap(ad_blood, random_state=312)

Uh oh - the UMAP embedding displays disconnected clusters of cells. This indicates that the integration didn’t perform very well, though other dimension reduction algorithms might perform better.

Code

sc.pl.embedding(
    ad_blood, 
    basis='umap', 
    color='celltype',
    title='', 
    frameon=True, 
    size=30, 
    alpha=0.75, 
    show=False
)
plt.gca().set_xlabel('UMAP 1')
plt.gca().set_ylabel('UMAP 2')
plt.show()

Force-directed graph embedding

Code

sc.tl.draw_graph(
    ad_blood, 
    layout='fr',  
    random_state=312,
    n_jobs=2
)

The FR embedding shows a disconnected cluster of monocytes as well. In addition, the progression from HSCs to progenitor / mature states is not well-represented.

Code

sc.pl.draw_graph(
    ad_blood, 
    color='celltype', 
    title='', 
    alpha=0.75, 
    size=30, 
    show=False
)
plt.gca().set_xlabel('FR 1')
plt.gca().set_ylabel('FR 2')
plt.show()

Figure 8: Harmony-based force-directed graph embedding

Diffusion map embedding

Code

sc.tl.diffmap(
    ad_blood, 
    random_state=312, 
    n_comps=16
)
ad_blood.obsm['X_diffmap_old'] = ad_blood.obsm['X_diffmap']
ad_blood.obsm['X_diffmap'] = ad_blood.obsm['X_diffmap'][:, 1:]

The diffusion map embedding is also fairly disconnected and has a tiny outlier cluster.

Code

sc.pl.diffmap(
    ad_blood, 
    color='celltype', 
    title='', 
    alpha=0.75, 
    size=30, 
    show=False
)
plt.gca().set_xlabel('DC 1')
plt.gca().set_ylabel('DC 2')
plt.show()

Figure 9: Harmony-based diffusion map embedding

Integration with `BBKNN`

Paper, Docs

Batch-specific SNN estimation

Next we try the BBKNN algorithm which, instead of producing an integrated embedding (as do the other methods) produces a batch-corrected neighborhood graph. We can then use that graph structure to generate UMAP and other embeddings.

Code

sc.external.pp.bbknn(
  ad_blood, 
  batch_key='fetal_ID',
  use_annoy=False,
  metric='cosine',
  pynndescent_random_state=312,
  pynndescent_n_neighbors=20
)

WARNING: consider updating your call to make use of `computation`

UMAP embedding

Code

sc.tl.umap(ad_blood, random_state=312)

The UMAP is somewhat smoothly-connected, but the biological sequence of celltypes isn’t correct.

Code

sc.pl.embedding(
    ad_blood, 
    basis='umap', 
    color='celltype',
    title='', 
    frameon=True, 
    size=30, 
    alpha=0.75, 
    show=False
)
plt.gca().set_xlabel('UMAP 1')
plt.gca().set_ylabel('UMAP 2')
plt.show()

Force-directed graph embedding

Code

sc.tl.draw_graph(
    ad_blood, 
    layout='fr',  
    random_state=312,
    n_jobs=2
)

The FR embedding suffers from the same issue.

Code

sc.pl.draw_graph(
    ad_blood, 
    color='celltype', 
    title='', 
    alpha=0.75, 
    size=30, 
    show=False
)
plt.gca().set_xlabel('FR 1')
plt.gca().set_ylabel('FR 2')
plt.show()

Figure 11: BBKNN-based force-directed graph embedding

Diffusion map embedding

Code

sc.tl.diffmap(
    ad_blood, 
    random_state=312, 
    n_comps=16
)
ad_blood.obsm['X_diffmap_old'] = ad_blood.obsm['X_diffmap']
ad_blood.obsm['X_diffmap'] = ad_blood.obsm['X_diffmap'][:, 1:]

The diffusion map embedding also misplaces the monocyte phenotype.

Code

sc.pl.diffmap(
    ad_blood, 
    color='celltype', 
    title='', 
    alpha=0.75, 
    size=30, 
    show=False
)
plt.gca().set_xlabel('DC 1')
plt.gca().set_ylabel('DC 2')
plt.show()

Figure 12: BBKNN-based diffusion map embedding

Integration with `Scanorama`

Paper, Docs

Batch-correcting integration

The last method we’ll try is Scanorama. Like scVi and Harmony this algorithm produces a corrected low-dimensional embedding.

Code

sc.external.pp.scanorama_integrate(
  ad_blood, 
  key='fetal_ID', 
  basis='X_pca', 
  knn=20
)

[[0. 1.]
 [0. 0.]]
Processing datasets F11_female_12+3PCW <=> F17_male_9+1PCW

SNN graph estimation

We identify \(k = 20\) NNs in the integrated space.

Code

sc.pp.neighbors(
    ad_blood, 
    n_neighbors=20,
    n_pcs=None,  
    metric='cosine', 
    random_state=312, 
    use_rep='X_scanorama'
)

UMAP embedding

Code

sc.tl.umap(ad_blood, random_state=312)

With UMAP the celltype progression is represented correctly, though the transition from the monocyte-macrophage phenotype to the mature Kupffer cells is a bit wonky.

Code

sc.pl.embedding(
    ad_blood, 
    basis='umap', 
    color='celltype',
    title='', 
    frameon=True, 
    size=30, 
    alpha=0.75, 
    show=False
)
plt.gca().set_xlabel('UMAP 1')
plt.gca().set_ylabel('UMAP 2')
plt.show()

Figure 13: Scanorama-based UMAP embedding

Force-directed graph embedding

Code

sc.tl.draw_graph(
    ad_blood, 
    layout='fr',  
    random_state=312,
    n_jobs=2
)

The FR embedding is a bit worse and isn’t very smoothly connected.

Code

sc.pl.draw_graph(
    ad_blood, 
    color='celltype', 
    title='', 
    alpha=0.75, 
    size=30, 
    show=False
)
plt.gca().set_xlabel('FR 1')
plt.gca().set_ylabel('FR 2')
plt.show()

Figure 14: Harmony-based force-directed graph embedding

Diffusion map embedding

Code

sc.tl.diffmap(
    ad_blood, 
    random_state=312, 
    n_comps=16
)
ad_blood.obsm['X_diffmap_old'] = ad_blood.obsm['X_diffmap']
ad_blood.obsm['X_diffmap'] = ad_blood.obsm['X_diffmap'][:, 1:]

The diffusion map embedding is mostly OK, but there’s a weird outlier cluster of monocytes located at the minimum of the first component.

Code

sc.pl.diffmap(
    ad_blood, 
    color='celltype', 
    title='', 
    alpha=0.75, 
    size=30, 
    show=False
)
plt.gca().set_xlabel('DC 1')
plt.gca().set_ylabel('DC 2')
plt.show()

Figure 15: Scanorama-based diffusion map embedding

Conclusions

Overall, it seems that the scVI latent representation of the data performed the best. As for embeddings of that integrated space, I would probably choose the force-directed graph layout, though UMAP also performed well. The primary drawback of this method isn’t readily apparent with this dataset - computation time. I’ve experienced long runtimes (1h+) on even medium-sized datasets of around 25-40k cells. This can make it difficult to experiment and tweak the algorithm’s parameters, but overall it tends to perform well without much tuning. As for non-deep learning methods, I would subjectively say that Scanorama provided the second-best embeddings, though I’ve also had good experiences with the R implementation of Harmony. As always, it’s important to try multiple methods and compare results both visually and quantitatively.

Session info

Code

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.2.1 (2022-06-23)
 os       macOS Big Sur ... 10.16
 system   x86_64, darwin17.0
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/New_York
 date     2023-12-08
 pandoc   3.1.9 @ /usr/local/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package          * version date (UTC) lib source
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 cli                3.6.1   2023-03-23 [1] CRAN (R 4.2.0)
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 leiden             0.4.2   2022-05-09 [1] CRAN (R 4.2.0)
 lifecycle          1.0.3   2022-10-07 [1] CRAN (R 4.2.0)
 listenv            0.8.0   2019-12-05 [1] CRAN (R 4.2.0)
 lmtest             0.9-40  2022-03-21 [1] CRAN (R 4.2.0)
 magrittr           2.0.3   2022-03-30 [1] CRAN (R 4.2.0)
 MASS               7.3-60  2023-05-04 [1] CRAN (R 4.2.0)
 Matrix           * 1.6-3   2023-11-14 [1] CRAN (R 4.2.1)
 matrixStats        0.62.0  2022-04-19 [1] CRAN (R 4.2.0)
 mime               0.12    2021-09-28 [1] CRAN (R 4.2.0)
 miniUI             0.1.1.1 2018-05-18 [1] CRAN (R 4.2.0)
 munsell            0.5.0   2018-06-12 [1] CRAN (R 4.2.0)
 nlme               3.1-159 2022-08-09 [1] CRAN (R 4.2.0)
 parallelly         1.36.0  2023-05-26 [1] CRAN (R 4.2.0)
 patchwork          1.1.2   2022-08-19 [1] CRAN (R 4.2.0)
 pbapply            1.5-0   2021-09-16 [1] CRAN (R 4.2.0)
 pillar             1.9.0   2023-03-22 [1] CRAN (R 4.2.0)
 pkgconfig          2.0.3   2019-09-22 [1] CRAN (R 4.2.0)
 plotly             4.10.0  2021-10-09 [1] CRAN (R 4.2.0)
 plyr               1.8.7   2022-03-24 [1] CRAN (R 4.2.0)
 png                0.1-7   2013-12-03 [1] CRAN (R 4.2.0)
 polyclip           1.10-0  2019-03-14 [1] CRAN (R 4.2.0)
 progressr          0.10.1  2022-06-03 [1] CRAN (R 4.2.0)
 promises           1.2.0.1 2021-02-11 [1] CRAN (R 4.2.0)
 purrr              1.0.2   2023-08-10 [1] CRAN (R 4.2.0)
 R6                 2.5.1   2021-08-19 [1] CRAN (R 4.2.0)
 RANN               2.6.1   2019-01-08 [1] CRAN (R 4.2.0)
 RColorBrewer       1.1-3   2022-04-03 [1] CRAN (R 4.2.0)
 Rcpp               1.0.9   2022-07-08 [1] CRAN (R 4.2.0)
 RcppAnnoy          0.0.19  2021-07-30 [1] CRAN (R 4.2.0)
 RcppHNSW           0.4.1   2022-07-18 [1] CRAN (R 4.2.0)
 reshape2           1.4.4   2020-04-09 [1] CRAN (R 4.2.0)
 reticulate       * 1.28    2023-01-27 [1] CRAN (R 4.2.0)
 rlang              1.1.2   2023-11-04 [1] CRAN (R 4.2.1)
 rmarkdown          2.16    2022-08-24 [1] CRAN (R 4.2.0)
 ROCR               1.0-11  2020-05-02 [1] CRAN (R 4.2.0)
 rprojroot          2.0.3   2022-04-02 [1] CRAN (R 4.2.0)
 RSpectra           0.16-1  2022-04-24 [1] CRAN (R 4.2.0)
 Rtsne              0.16    2022-04-17 [1] CRAN (R 4.2.0)
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 scattermore        1.2     2023-06-12 [1] CRAN (R 4.2.0)
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 sessioninfo        1.2.2   2021-12-06 [1] CRAN (R 4.2.0)
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 shiny              1.7.2   2022-07-19 [1] CRAN (R 4.2.0)
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 spam               2.9-1   2022-08-07 [1] CRAN (R 4.2.0)
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 spatstat.geom      3.0-3   2022-10-25 [1] CRAN (R 4.2.0)
 spatstat.random    3.0-1   2022-11-03 [1] CRAN (R 4.2.0)
 spatstat.sparse    3.0-0   2022-10-21 [1] CRAN (R 4.2.0)
 spatstat.utils     3.0-1   2022-10-19 [1] CRAN (R 4.2.0)
 stringi            1.7.8   2022-07-11 [1] CRAN (R 4.2.0)
 stringr            1.5.0   2022-12-02 [1] CRAN (R 4.2.0)
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 tidyr              1.3.0   2023-01-24 [1] CRAN (R 4.2.0)
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 uwot               0.1.16  2023-06-29 [1] CRAN (R 4.2.0)
 vctrs              0.6.3   2023-06-14 [1] CRAN (R 4.2.0)
 viridisLite        0.4.1   2022-08-22 [1] CRAN (R 4.2.0)
 withr              2.5.2   2023-10-30 [1] CRAN (R 4.2.1)
 xfun               0.32    2022-08-10 [1] CRAN (R 4.2.0)
 xtable             1.8-4   2019-04-21 [1] CRAN (R 4.2.0)
 yaml               2.3.5   2022-02-21 [1] CRAN (R 4.2.0)
 zoo                1.8-10  2022-04-15 [1] CRAN (R 4.2.0)

 [1] /Library/Frameworks/R.framework/Versions/4.2/Resources/library

─ Python configuration ───────────────────────────────────────────────────────
 python:         /Users/jack/Desktop/PhD/Research/Python_Envs/personal_site/bin/python
 libpython:      /usr/local/opt/python@3.11/Frameworks/Python.framework/Versions/3.11/lib/python3.11/config-3.11-darwin/libpython3.11.dylib
 pythonhome:     /Users/jack/Desktop/PhD/Research/Python_Envs/personal_site:/Users/jack/Desktop/PhD/Research/Python_Envs/personal_site
 version:        3.11.6 (main, Nov  2 2023, 04:52:24) [Clang 14.0.3 (clang-1403.0.22.14.1)]
 numpy:          /Users/jack/Desktop/PhD/Research/Python_Envs/personal_site/lib/python3.11/site-packages/numpy
 numpy_version:  1.23.5
 
 NOTE: Python version was forced by use_python function

──────────────────────────────────────────────────────────────────────────────

Introduction

Libraries

R

Python

Theme for matplotlib

Data

Read data into Python

Preprocessing

Integration with scVI

Train scVI model

SNN graph estimation

UMAP embedding

Force-directed graph embedding

Diffusion map embedding

Integration with Harmony

Normalization

PCA embedding

PCA correction with Harmony

SNN graph estimation

UMAP embedding

Force-directed graph embedding

Diffusion map embedding

Integration with BBKNN

Batch-specific SNN estimation

UMAP embedding

Force-directed graph embedding

Diffusion map embedding

Integration with Scanorama

Batch-correcting integration

SNN graph estimation

UMAP embedding

Force-directed graph embedding

Diffusion map embedding

Conclusions

Session info

Theme for `matplotlib`

Integration with `scVI`

Train `scVI` model

Integration with `Harmony`

PCA correction with `Harmony`

Integration with `BBKNN`

Integration with `Scanorama`